基于三段式特征选择策略的脑电情感识别算法SEE

doi:10.13229/j.cnki.jdxbgxb20210115

摘要/Abstract

摘要：

情感可以通过脑电信号中的隐藏模式来识别，基于众多的脑电通道提取的脑电特征数量庞大，使情感识别任务非常复杂。针对上述问题，提出了基于三段式特征选择策略的脑电情感识别算法（SEE）。本文从时域、频域、空间域系统地提取脑电特征，基于提取的脑电特征集合，首先通过t检验去除类间无显著差异的特征，再使用递归特征消除策略进行目标相关的特征选择，最后通过顺序后向特征选择策略确定最终的特征子集用于情感识别。实验结果表明：相较于其他方法，本文构建的模型有更好的情感识别能力。与现有的特征选择算法相比，SEE具有较低的时间复杂度并且能筛选出较优的特征子集。此外，分析了与情感相关的脑电通道和频带，实验结果体现了一定的生理学意义，为开发情绪靶向的脑电设备提供可能。

关键词: 计算机应用, 脑电图, 情感识别, 特征工程, 特征选择

Abstract:

Emotions can be recognized through the hidden patterns in the EEG signals. The large number of EEG features extracted based on numerous EEG channels makes the task of emotion recognition very complex. To solve above problems， An EEG emotion recognition algorithm (SEE) based on three-stage feature selection strategy is proposed. EEG features were systematically extracted from time domain， frequency domain and spatial domain in this study. Based on the extracted EEG feature set， the features with no significant difference between classes were removed by t-test firstly， and then the recursive feature elimination strategy was used to select target related features. Finally， the final feature set was determined by the sequential backward feature selection strategy for the emotion recognition. The experimental results show that the model constructed in this study has better emotion recognition ability than other methods. Compared with the existing feature selection algorithms， SEE can filter out better feature subset with a low time complexity. In addition， emotion-associated EEG channels and frequency bands were detected. The experimental results show physiological significance of emotion， which may facilitate the development of emotion-targeted EEG devices.

Key words: computer application, electroencephalogram, emotion recognition, feature engineering, feature selection

中图分类号:

TP391

周丰丰,朱海洋. 基于三段式特征选择策略的脑电情感识别算法SEE[J]. 吉林大学学报(工学版), 2022, 52(8): 1834-1841.

Feng-feng ZHOU,Hai-yang ZHU. SEE： sense EEG⁃based emotion algorithm via three⁃step feature selection strategy[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1834-1841.

图/表 8

表1

表2

表3

图1

图2

图3

图4

表4

参考文献 20

1	Fraschini M, Meli M, Demuru M, et al. EEG fingerprints under naturalistic viewing using a portable device[J]. Sensors, 2020, 20(22): 20226565.
2	方明, 陈文强. 结合残差网络及目标掩膜的人脸微表情识别[J]. 吉林大学学报: 工学版, 2021, 51(1): 303-313.
	Fang Ming, Chen Wen-qiang. Face micro-expression recognition based on ResNet with object mask[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(1): 303-313.
3	Li X, Song D, Zhang P, et al. Exploring EEG features in cross-subject emotion recognition[J]. Frontiers in Neuroscience, 2018, 12(1): 162-176.
4	代琨, 于宏毅, 仇文博, 等. 基于SVM的网络数据无监督特征选择算法[J]. 吉林大学学报: 工学版, 2015, 45(2): 576-582.
	Dai Kun, Yu Hong-yi, Qiu Wen-bo, et al. Unsupervised feature selection algorithm based on support vector machine for network data[J]. Journal of Jilin University(Engineering and Technology Edition), 2015, 45(2): 576-582.
5	Gupta V, Chopda M D, Pachori R B. Cross-subject emotion recognition using flexible analytic wavelet transform from EEG signals[J]. IEEE Sensors Journal, 2019, 19(6): 2266-2274.
6	Duan R N, Zhu J Y, Lu B L. Differential entropy feature for EEG-based emotion classification[C]∥6th International IEEE/EMBS Conference on Neural Engineering, San Diego, USA, 2013: 81-84.
7	Zheng W L, Lu B L. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks[J]. IEEE Transactions on Autonomous Mental Development, 2015, 7(3): 162-175.
8	Zhang J H, Chen M, Zhao S K, et al. Relieff-based EEG sensor selection methods for emotion recognition[J]. Sensors, 2016, 16(10): 1558-1572.
9	周怡娜, 董宏丽, 张勇, 等. 基于VMD去噪和散布熵的管道信号特征提取方法[J]. 吉林大学学报: 工学版, 2022, 52(4): 959-969.
	Zhou Yi-na, Dong Hong-li, Zhang Yong, et al. Feature extraction method of pipeline signals based on VMD de-noising and dispersion entropy[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 959-969.
10	Yang S, Li B, Zhang Y, et al. Selection of features for patient-independent detection of seizure events using scalp EEG signals[J]. Computers in Biology and Medicine, 2020, 119: 103671.
11	张冠华, 余旻婧, 陈果, 等. 面向情绪识别的脑电特征研究综述[J]. 中国科学: 信息科学, 2019, 49(9):1097-1118.
	Zhang Guan-hua, Yu Min-jing, Chen Guo, et al. A review of EEG features for emotion recognition[J]. SCIENTIA SINICA Informationis, 2019, 49(9): 1097-1118.
12	Koelstra S, Muhl C, Soleymani M, et al. DEAP: a database for emotion analysis; using physiological signals[J]. IEEE Transactions on Affective Computing, 2012, 3(1): 18-31.
13	Arnau-Gonzalez P, Arevalillo-Herraez M, Ramzan N. Fusing highly dimensional energy and connectivity features to identify affective states from EEG signals[J]. Neurocomputing, 2017, 244: 81-89.
14	Yang B, Han X, Tang J. Three class emotions recognition based on deep learning using staked autoencoder[C]∥10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Shanghai, China, 2017: 1-5.
15	Wu X, Zheng W L, Lu B L. Identifying functional brain connectivity patterns for EEG-based emotion recognition[C]∥9th International IEEE/EMBS Conference on Neural Engineering, San Francisco, USA, 2019: 235-238.
16	Zhang W, Wang F, Jiang Y, et al. Cross-subject EEG-Based emotion recognition with deep domain confusion[C]∥International Conference on Intelligent Robotics and Applications, Shenyang, China, 2019: 558-570.
17	Sarlo M, Buodo G, Poli S, et al. Changes in EEG alpha power to different disgust elicitors: the specificity of mutilations[J]. Neuroscience Letters, 2005, 382(3): 291-296.
18	Li M, Lu B L. Emotion classification based on gamma-band EEG[C]∥Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, USA, 2009: 1223-1226.
19	Saarimki H, Gotsopoulos A, Jskelinen I P, et al. Discrete neural signatures of basic emotions[J]. Cerebral Cortex, 2015, 26(6): 2563-2573.
20	Lin Y P, Yang Y H, Jung T P. Fusion of electroencephalographic dynamics and musical contents for estimating emotional responses in music listening[J]. Frontiers in Neuroscience, 2014, 8: 94-107.

相关文章 15

[1]	周丰丰,张亦弛. 基于稀疏自编码器的无监督特征工程算法BioSAE[J]. 吉林大学学报(工学版), 2022, 52(7): 1645-1656.
[2]	王斌,何丙辉,林娜,王伟,李天阳. 基于随机森林特征选择的茶园遥感提取[J]. 吉林大学学报(工学版), 2022, 52(7): 1719-1732.
[3]	王军,徐彦惠,李莉. 低能耗支持完整性验证的数据融合隐私保护方法[J]. 吉林大学学报(工学版), 2022, 52(7): 1657-1665.
[4]	王生生,姜林延,杨永波. 基于最优传输特征选择的医学图像分割迁移学习[J]. 吉林大学学报(工学版), 2022, 52(7): 1626-1638.
[5]	康耀龙,冯丽露,张景安,陈富. 基于谱聚类的高维类别属性数据流离群点挖掘算法[J]. 吉林大学学报(工学版), 2022, 52(6): 1422-1427.
[6]	王文军,余银峰. 考虑数据稀疏的知识图谱缺失连接自动补全算法[J]. 吉林大学学报(工学版), 2022, 52(6): 1428-1433.
[7]	陈雪云,贝学宇,姚渠,金鑫. 基于G⁃UNet的多场景行人精确分割与检测[J]. 吉林大学学报(工学版), 2022, 52(4): 925-933.
[8]	方世敏. 基于频繁模式树的多来源数据选择性集成算法[J]. 吉林大学学报(工学版), 2022, 52(4): 885-890.
[9]	李大湘,陈梦思,刘颖. 基于STA⁃LSTM的自发微表情识别算法[J]. 吉林大学学报(工学版), 2022, 52(4): 897-909.
[10]	魏晓辉,苗艳微,王兴旺. Rhombus sketch：自适应和准确的流数据sketch[J]. 吉林大学学报(工学版), 2022, 52(4): 874-884.
[11]	刘铭,杨雨航,邹松霖,肖志成,张永刚. 增强边缘检测图像算法在多书识别中的应用[J]. 吉林大学学报(工学版), 2022, 52(4): 891-896.
[12]	毛琳,任凤至,杨大伟,张汝波. 双向特征金字塔全景分割网络[J]. 吉林大学学报(工学版), 2022, 52(3): 657-665.
[13]	王雪,李占山,吕颖达. 基于多尺度感知和语义适配的医学图像分割算法[J]. 吉林大学学报(工学版), 2022, 52(3): 640-647.
[14]	欧阳继红,郭泽琪,刘思光. 糖尿病视网膜病变分期双分支混合注意力决策网络[J]. 吉林大学学报(工学版), 2022, 52(3): 648-656.
[15]	王学智,李清亮,李文辉. 融合迁移学习的土壤湿度预测时空模型[J]. 吉林大学学报(工学版), 2022, 52(3): 675-683.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

方法	准确率	精确率	召回率	F1分数
基线	58.0	-	-	-
文献［13］	69.6	-	-	-
SEE-LR	70.2	72.4	81.4	76.7
SEE-SVM （Linear）	69.5	72.4	79.5	75.8
SEE-SVM （RBF）	71.0	74.2	79.3	76.7

方法	准确率	精确率	召回率	F1分数
基线	62.0	-	-	-
文献［13］	67.7	-	-	-
SEE-LR	69.5	72.6	78.4	75.3
SEE-SVM （Linear）	67.4	69.3	79.7	74.1
SEE-SVM （RBF）	70.0	72.5	78.8	75.5

方法	准确率	宏精确率	宏召回率	宏F1分数
文献［14］	85.5	-	-	-
文献［15］	87.0	-	-	-
文献［16］	90.6	-	-	-
SEE-LR	91.8	89.4	87.4	88.3
SEE-SVM（Linear）	90.2	88.1	86.3	86.1
SEE-SVM（RBF）	92.5	90.5	88.5	89.5

通道数	准确率/%
14	90.4
62	91.8